1. Field of the Invention (Technical Field)
The present invention relates to sensor and process analysis and diagnosis, especially through use of Bayesian Belief Networks (BBNs).
2. Background Art
The following patents discuss control systems:
U.S. Pat. No. 5,726,915, entitled xe2x80x9cAutomated System for Testing an Imaging Sensor,xe2x80x9d to Prager, et al., issued Mar. 10, 1998. This patent discloses univariate techniques fortesting imaging sensors. This patent assumes a known signal. The method also uses a frequency-domain analysis step that forces collection of large amounts of data and shifting to a frequency domain. The method is a test suitable to determine if a sensor is suspect, which is likely to involve taking the sensor off-line and then carrying out this procedure.
U.S. Pat. No. 5,629,872, entitled, xe2x80x9cSystem for Monitoring an Industrial Process and Determining Sensor Status, to Gross, et al., issued May 13, 1997 with a terminal disclaimer to U.S. Pat. No. 5,223,207. This patent discloses univariate techniques for system monitoring. The disclosed method also uses a frequency-domain analysis step that forces collection of large amounts of data and shifting to frequency domain. The method is a test suitable to determine if a sensor is suspect, which is likely to involve taking the sensor off-line and then carring out this procedure.
U.S. Pat. No. 5,661,666, entitled xe2x80x9cConstant False Probability Data Fusion System,xe2x80x9d to Pawlak, issued Aug. 26, 1997. The thrust of this patent""s disclosed method is to compare input sensor data against the values in a lookup table. The threshold lookup table provides two thresholds. A xe2x80x9cdata fusionxe2x80x9d processor uses the sensor xe2x80x9cdecisionsxe2x80x9d to generate a log-likelihood ratio, which is used as a test existence metric. The values of the threshold lookup table appear as weighted sums, which are normalized to represent in the probability domain. The invention provides sensor fault reconciliation only, which is based on a weighted sum and table lookup method. The method does not detect and isolate specific sensor faults.
U.S. Pat. No. 5,223,207, entitled xe2x80x9cExpert System for Online Surveillance of Nuclear Reactor Coolant Pumps,xe2x80x9d to Gross, et al., issued Jun. 29, 1993. This patent disclosed expert system technology for online surveillance of nuclear reactor coolant pumps through use of an artificial intelligence inference engine (an expert system) for early detection of pump or sensor degradation. The degradation is based on a sequential probability ratio test (SPRT). The invention provides sensor fault detection only and serves only as an early alert system to allow an xe2x80x9corderly shutdown of the pumpxe2x80x9d to avert serious damage to it. The method does not isolate the specific sensor fault. Further, it does not reconcile sensor data.
U.S. Pat. No. 5,548,378, entitled xe2x80x9cImage Operating Apparatus Providing Image Stabilization Control,xe2x80x9d to Ogata, et al., issued Aug. 20, 1996. This patent discloses a simple principle of comparing sensor-input data with certain standard values stored in the sensor controller. With this simple comparative method, a fault is detected as a disagreement between the sensor data and the stored standard values. The method is developed for a specific application, namely, use of photoelectric sensor for conveyor belt control (i.e., spacing of bottles on a conveyor belt).
U.S. Pat. No. 5,267,277, entitled xe2x80x9cIndicator System for Advanced Nuclear Plant Control Complex,xe2x80x9d to Scarola, et al., issued Nov. 30, 1993. This patent discloses a collection of tools to centralized signal display in a central location for a nuclear power plant, having concise information processing and display, reliable architecture and hardware, and easily maintainable components, while eliminating operator information overload. This method provides for a rapid response to changes in plant parameters and component control system. The xe2x80x9ccomplexxe2x80x9d includes six major systems: (1) the control center panels, (2) the data processing system, (3) the discrete indication and alarm system, (4) the component control system consisting of the engineered safeguard function component controls, (5) the plant protection system, and (6) the power control system. The six systems collect data from the plant, xe2x80x9cefficientlyxe2x80x9d present the required information to the operator, perform all automatic functions and provide for direct manual control of the plant components.
U.S. Pat. No. 5,680,409, entitled xe2x80x9cMethod and Apparatus for Detecting and Identifying Faulty Sensors in a Process,xe2x80x9d to Qin, et al., issued Oct. 21, 1997. This patent discloses a principal component analysis methods to detect a faulty sensor from the signals provided to it through a set of sensors. It also develops a validity index, which is a ratio of two residuals, to isolate the faulty sensor. A residual is calculated from the difference between the sensor output and the average of all sensor output signals. The principal component analysis is a transformation technique that converts a set of correlated sensor measurements into a set of uncorrelated variables. The effect of this transformation is to rotate the coordinate system in a way that results in the alignment of information represented by the sensor measurement on a fewer number of axes than the original coordinate system. This transformation results in a comparison of the variables by allowing those variables that are highly correlated with one another to be treated as a single variable.
U.S. Pat. No. 5,130,936, entitled xe2x80x9cMethod and Apparatus for Diagnostic Testing Including a Neural Network for Determining Testing Sufficiency,xe2x80x9d to Sheppard, et al., issued Jul. 14, 1992. This patent discloses a combination of evidence theory and neural networks for improved diagnostic testing and determining the sufficiency of testing in diagnostic testing. The method receives input corresponding to at least one predetermined parameter of the system corresponding to its condition and produces a ranked set of diagnostic signals. Further, the system determines the sufficiency of the signal to ensure the validity of its diagnosis.
U.S. Pat. No. 5,715,178, entitled xe2x80x9cMethod of Validating Measurement Data of a Process Parameter from a Plurality of Individual Sensor Inputs,xe2x80x9d to Scarola, et al., issued Feb. 3, 1998. This patent discloses a method to reduce information overload on a plant operator by providing means to display information in a concise, reliable, and easily maintainable manner. This patent""s disclosure is aimed at improving overall effectiveness of a control room complex by providing novel designs for the alarm indicators, alarm processors, displays and the like. The patent also discloses a knowledge-based heuristic algorithm based on the explicit calculation of residuals among redundant sensors that measure a same variable.
U.S. Pat. No. 5,237,518, entitled xe2x80x9cOptimization Method for Adaptive Sensor Reading Scheduling and Delayed Alarm Evaluation in Real-Time Diagnostic Systems,xe2x80x9d to Sztipanovits, et al., issued Aug. 17, 1993. This patent discloses an optimization algorithm for use in an automated fault diagnostic system. It aims at scheduling an optimal sequence of evaluations of alarms that may be triggered by the diagnostic system.
None of the preceding patents disclose use of Bayesian belief networks for sensor diagnosis.
The present invention comprises a method for diagnosis of sensors comprising: providing at least one sensor-status node wherein each sensor-status node comprises a known probability table; providing at least one process-variable node wherein each process-variable node comprises a known probability table; providing at least one sensor-reading node wherein each sensor-reading node comprises a probability table conditional on at least two known probability tables; providing the at least one sensor-reading node with at least one sensor reading; inferring a status of at least one sensor. The method of the present invention, more specifically, comprises at least one Bayesian belief network. The method of the present invention is additionally useful for estimating at least one value of at least one process variable.
While relying on at least one Bayesian belief network, the method of the present invention comprises updating at least one known probability table after providing at least one sensor-reading node with at least one sensor reading. Monitoring of at least one updated table is within the scope of the present invention and is useful for assessing sensor fault detection, sensor fault classification, process fault detection, and/or process fault classification.
The method of the present invention also comprises inferring through process modeling wherein process modeling comprises continuous-value models, discrete-value models, linearized models, neural network models, fuzzy logic models, steady-state models, unsteady-state models, static models, and/or dynamic models.
According to the method of the present invention, inputting of data, i.e., information of any sort, can be through an external observer. Of course, providing at least one sensor reading comprises providing a reading from any type of sensor. More specifically, sensors such as, for example, but not limited to, are suitable: temperature sensors, concentration sensors, pH sensors, level sensors, flowrate sensors, and/or volume sensors.
The present invention also comprises an apparatus for sensor and/or process diagnosis. The apparatus comprises calculation means, input means, storage means, and output means. Calculation means comprises means for processing data, such as, for example, calculating probabilities of a Bayesian belief network wherein the network comprises at least one sensor-status node, at least one process-variable node, and at least one sensor-reading node. Digital and/or analog computers are suitable for providing for a calculation means for processing data. Input means comprises means for inputting information to the calculation means. Input means is achieved, for example, through connections for transmission of digital and/or analog information, through wire, fiber, radiowaves and the like. Information input to the apparatus includes information or data related to, but not limited to, processes, sensors, feedback, measurements, and the like. Such information can comprise data from an external observer and/or information from a reading from any sensor, for example, but not limited to, information from at least one of the following: temperature sensors, concentration sensors, pH sensors, level sensors, flowrate sensors, and volume sensors. Storage means comprises at least one manner for storing information, such as, but not limited to, digital and/or analog devices commonly used in the digital and/or analog computer industry. For example, RAM, floppy drives, hard drives, optical drives, and the like and their associated storage medium are suitable for use with the apparatus of the present invention. Output means comprises at least one means for transmitting information to, for example, an operator, a display device, a printer, a radio transmitter, and the like. In some instances, such information is used to change at least one process parameter or setting, thereby achieving feedback to a process.
The apparatus of the present invention comprises calculation means that also comprise solution means for solving at least one process model; estimating means for estimating at least one value of at least one process variable; updating means for updating at least one known probability table; monitoring means for monitoring of at least one updated probability table for assessing at least one member selected from the group consisting of sensor fault detection, sensor fault classification, process fault detection, and process fault classification; and, in general, process modeling. According to preferred embodiments of the present invention, process modeling comprises at least one of the following: continuous-value models, discrete-value models, linearized models, neural network models, fuzzy logic models, steady-state models, unsteady-state models, static models, and/or dynamic models.
A primary object of the present invention is to diagnose sensor and/or process faults through use of a Bayesian belief network.
A primary advantage of the present invention is sensor and/or process fault detection, isolation and accommodation.
Other objects, advantages and novel features, and further scope of applicability of the present invention will be set forth in part in the detailed description to follow, taken in conjunction with the accompanying drawings, and in part will become apparent to those skilled in the art upon examination of the following, or may be learned by practice of the invention. The objects and advantages of the invention may be realized and attained by means of the instrumentalities and combinations particularly pointed out in the appended claims.